CITIfile 0.1.6

To parse CITI format file to xarray.

CITIfile

Read CITI format file by python.

Introduction

CITIfile is a tiny library that allows you to read CITI format file by using Python.

Installation

pip install -U CITIfile

Tutorial

CITIfile is easy to use:

from CITIfile import read_citifile


data = read_citifile("data.citi")

Then, it will parse the data file and return a xarray.Dataset object. Like the following:

<xarray.Dataset>
Dimensions:   (C_d2: 3, L_load: 6, freq: 6)
Coordinates:
  * L_load    (L_load) float64 20.0 21.0 22.0 23.0 24.0 25.0
  * C_d2      (C_d2) float64 1.0 1.5 2.0
  * freq      (freq) float64 3e+07 3.1e+07 3.2e+07 3.3e+07 3.4e+07 3.5e+07
Data variables:
    S[1,1]    (L_load, C_d2, freq) complex128 (0.0517763713-0.00941470346j) ... (0.0593002475-0.0233189028j)
    S[1,2]    (L_load, C_d2, freq) complex128 (0.771829001+0.00291050315j) ... (0.774683786+0.0184790199j)
    S[2,1]    (L_load, C_d2, freq) complex128 (0.771829001+0.00291050315j) ... (0.774683786+0.0184790199j)
    S[2,2]    (L_load, C_d2, freq) complex128 (0.0517763713-0.00941470346j) ... (0.0593002475-0.0233189028j)

CITIfile parses and converts CITI format to xarray.Dataset object, because CITI format file stores data in multidimensional array format and xarray are designed to handle this kind of data.

See xarray documents to learn that how to manipulate xarray.Dataset object.

Displaying Coordinates and Data Vairables

print('Coordinates:')
for cname in data.coords:
    cdata = data.coords[cname].data
    print(f'- {cname}: {len(cname)} ({cdata}, {cdata.dtype})')

print('Data:')
for vname in data.data_vars:
    vdata = data.data_vars[vname]
    print(f'- {vname}: {vdata.dtype}')

This displays the names and types of the corrdinates and data variables, as well as the available corrdinate values (which are numpy.ndarray).

Extracting Usable 2D Data

In this example, we extract S[1,1] vs. freq, at some given coordinates for C_load and C_d2:

# select the data variable at the given corrdinates
data_slice = data.data_vars["S[1,1]"].sel(L_load=20, C_d2=1)

# now there is only one coordinate (freq) left as independent variable
x = data_slice.coords["freq"].data

# dependent variable (Y-axis)
y = data_slice.data

for px, py in zip(x, y):
    print(f"freq={px} -> S[1,1]={py}")

Both x and y are numpy.ndarray.

Websites

Main website: https://github.com/TitorX/CITIfile

CITIfile Definitions: http://literature.cdn.keysight.com/litweb/pdf/ads15/cktsim/ck2016.html

xarray document: http://xarray.pydata.org/en/stable/

Reports

Report bugs or ask questions at https://github.com/TitorX/CITIfile/issues.

Contact

Written by Shoukun Sun.

Email: [e-mail]